CN115907363A - Source-load multi-time scale optimization scheduling method based on comprehensive energy system - Google Patents
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Abstract
The invention discloses a source-load multi-time scale optimization scheduling method based on a comprehensive energy system, which comprises the steps that firstly, a thermal power generating unit is considered to be additionally provided with a carbon capture device at a source side to form coordination and coordination of a carbon capture power plant and renewable energy, and a load side is considered to solve the operation limitation of the carbon capture power plant under multiple time scales by considering various demand response resources, so that a two-stage low-carbon economic optimization scheduling model of source-load coordination in the day-day period is constructed; secondly, according to the response speed of different demand response resources to the power grid dispatching instruction, the demand response resources are divided into a first-level demand response resource and a second-level demand response resource. Finally, the improved IEEE-39 node is used as an example simulation, and the result shows that the scheduling method can coordinate source and load resources, reduce the system operation cost and reduce the carbon emission.
Description
Technical Field
The invention relates to the technical field of optimized operation of power systems, in particular to a source-load multi-time scale optimized scheduling method based on a comprehensive energy system.
Background
The comprehensive energy system breaks the barriers of the traditional independent operation modes of electricity, gas and heat energy sources, and has the advantages of multi-energy complementation, energy utilization efficiency improvement, energy cost reduction and carbon emission reduction. However, the current comprehensive energy system has the problems of high carbon emission, low renewable energy utilization rate and high total operation cost, so that the important problem that needs to be solved at present is to explore how to coordinate energy source side energy supply equipment and load side energy utilization equipment so as to reduce the carbon emission, improve the renewable energy consumption and reduce the total system cost.
At present, researches are considered to improve a conventional unit into a carbon capture power plant by utilizing a carbon capture and sequestration technology to reduce the carbon emission of the unit, improve the operation flexibility of the unit and improve the renewable energy consumption of the system, but the above documents ignore that an electric gas conversion device only works when the renewable energy is left, when the electric gas conversion device works, the amount of CO2 captured by the carbon capture device is less, and the problem of mismatching of the CO2 capture time and the utilization time exists. Some scholars construct a low-carbon economic dispatching model with mutually matched source load and load sides by considering generalized electric heating demand response. However, the above document only considers scheduling at a long time scale, and does not consider correcting a prediction error at a short time scale.
Disclosure of Invention
The invention aims to provide a low-carbon economic optimization scheduling method of a comprehensive energy system based on carbon capture aiming at solving the problems in the prior art and aiming at obtaining the lowest system operation cost and the total carbon emission of the system.
In order to solve the technical problems, the invention provides the following technical scheme: a source-load multi-time scale optimization scheduling method based on an integrated energy system comprises the following steps:
step A: firstly, source load and load are coordinated and matched, and demand response resources are divided into a first-level demand response resource and a second-level demand response resource according to the response speed of different demand response resources to a power grid dispatching instruction. The first-level demand response resources are used in the day-ahead scheduling stage, and the second-level demand response resources are used in the day-inside scheduling stage;
and B: a carbon capture power plant is formed by modifying a traditional thermal power generating unit with an additional carbon capture device on the source side, and the carbon capture power plant adopts a comprehensive flexible operation mode. The comprehensive flexible operation mode is that the carbon capture power plant is added with a flue gas bypass system and a solution storage to jointly act;
and C: the load side considers various demand response resources, the demand response resources comprise price type demand response resources and incentive type demand response resources, and the load user comprises a rigid load, a transferable load, an interruptible load and a replaceable load;
step D: acquiring initial carbon emission quota and actual carbon emission quota of the comprehensive energy system, and constructing a stepped carbon trading model of the comprehensive energy system according to the initial carbon emission quota and the actual carbon emission quota;
step E: on the basis of the step A, calling the resources on the two sides of the source side in the step B and the step C, considering the step-type carbon trading model in the step D, and establishing a source load coordination day-ahead-day internal two-stage low-carbon economic optimization scheduling model;
step F: and E, based on the source-load coordination day-ahead-day-intra-two-stage low-carbon economic optimization scheduling model in the step E, improving an IEEE-39 node as an example simulation, and solving the model by utilizing solver CPLEX software in MATLAB software.
Preferably, the integrated energy system in the step a comprises a wind turbine generator, a photovoltaic generator, an external power grid, a carbon capture power plant, a gas turbine, an electric-to-gas device, an electrolyzer device, a hydrogen fuel cell, an electric boiler, a carbon storage and liquid storage device, a rigid load and a flexible load.
Preferably, the demand response resource types in step a are divided according to the response speed of different demand response resources to the power grid dispatching instruction.
Preferably, the first-level comprehensive demand response in step a is: the speed of the comprehensive demand response resources responding to the power grid dispatching instruction is low, the response time is usually longer than 1h, and the comprehensive demand response resources need to be notified 24h in advance, so the comprehensive demand response resources are used in the day-ahead dispatching stage; secondary comprehensive demand response: the comprehensive demand response resources have high response speed to the power grid dispatching instruction, the response time is usually 5-15min, and the notification needs to be carried out 15-4 h in advance, so the comprehensive demand response resources are used in the day dispatching stage.
Preferably, the source side carbon capture power plant in the step B adopts a comprehensive flexible operation mode, the comprehensive flexible operation mode is that the carbon capture power plant is added with a flue gas bypass system and a solution storage to act together, and compared with a single liquid storage mode and a split-flow operation mode, the comprehensive flexible operation mode can automatically perform the CO operation mode according to the load requirements of different time periods 2 The carbon is discharged into the air, so that the scheduling flexibility is improved, and the peak clipping and valley filling can be realized, namely, the carbon capture energy consumption is transferred to the low valley period during the peak load; and the carbon capture energy consumption is increased in the load valley, so that the contradiction between the load demand and the carbon capture energy consumption can be solved, and the utilization of renewable energy sources is further improved.
Preferably, the load side in step C considers multiple demand response resources, where the demand response resources include two types, namely price type demand response resources and incentive type demand response resources, the price type demand response resources guide the user to perform reasonable power utilization behavior by changing the price of electric energy, so that the user changes the previous power utilization habit to generate good power utilization guidance, the price type demand response resources can change a load curve, and reduce the peak-valley difference of the power load, thereby reducing the power utilization cost of the user, and the user actively participates in the peak shaving of the system.
Preferably, the step-type carbon trading model in step D includes an initial carbon emission quota, an actual carbon emission quota, and a carbon emission price, and the carbon emission right trading amount participating in the carbon trading market can be found according to the carbon emission quota and the actual carbon emission amount.
Preferably, the comprehensive energy system source-load multi-time scale optimization scheduling method based on multi-energy complementation further includes constraints on each link, including power balance constraint, unit operation constraint, constraint on each device in the comprehensive energy system and tie line interaction power constraint.
The invention has the advantages that: the invention provides a source-load multi-time scale optimization operation mechanism analysis based on an integrated energy system, provides a source-load multi-time scale optimization scheduling method based on the integrated energy system, establishes a two-stage low-carbon economic optimization scheduling model of source-load coordination in the day-ahead and in the day, and analyzes the influence of the multi-time scale optimization scheduling method on the economic benefit and the environmental benefit of the integrated energy system according to research results.
The gas turbine connects the heat supply network with the gas network, and the electric gas conversion equipment converts electric energy into hydrogen energy, thereby realizing the coupling of electricity-heat-gas-hydrogen energy. If only the source side or the load side is considered independently, the low-carbon characteristic has limitation, the electricity utilization adjusting time period of a load user is limited, and the like. Therefore, the output plan of the unit is adjusted through source-load collaborative optimization, and finally the total carbon emission of the system is reduced.
Responding the demand according to the speed of different demand response resources responding to the power grid dispatching instructionResources are divided into two categories: the first-stage demand response is performed, the speed of the demand response resources responding to the power grid dispatching instruction is low, the response time is usually longer than 1h, and the demand response resources need to be notified 24h in advance, so the demand response resources are used in the day-ahead dispatching stage; and the secondary demand response resources have high response speed to the power grid dispatching command, the response time is usually 5-15min, and the notification needs to be carried out 15min-4h in advance, so the demand response resources are used in the day dispatching stage. The demand response resource can change a load curve, optimize the output of the carbon capture power plant, and reduce CO of the comprehensive energy system 2 The emission is improved to capture CO in a carbon capture power plant 2 An effective means of competence. In addition, in the two stages of the day ahead and the day in, the load can be reduced to reduce the unit output and reduce CO by using different types of demand response resources at the time of load peak 2 Discharge capacity; and the load is increased in the load valley to improve the consumption of renewable energy sources and reduce the light abandoning amount of the system. Price type demand response and first-stage demand response with low response speed to a power grid dispatching instruction are used in the day-ahead stage for a carbon capture power plant with a source side adopting a comprehensive flexible operation mode; and in the day period, a secondary demand response with a high response speed to the power grid dispatching command is used, so that the load and the rotary standby plan of the system are improved, and the defect that the rotary standby capability of the carbon capture power plant adopting a comprehensive flexible operation mode is weak is overcome, so that the carbon emission of the system is reduced, and the economic benefit of the system is improved.
Drawings
FIG. 1 is a schematic diagram of an integrated energy system structure in an integrated energy system source-load multi-time scale-based optimal scheduling method according to the present invention;
FIG. 2 is a schematic diagram of a principle of a comprehensive flexible operation mode of a carbon capture power plant on the energy source side of an integrated energy system in an optimized scheduling method based on the source-load multiple time scales of the integrated energy system according to the present invention;
FIG. 3 is a diagram of an improved IEEE-39 node power system architecture based on an integrated energy system source-load multi-time scale optimized scheduling method of the present invention;
FIG. 4 is a schematic flow chart of a source-load time scale optimized scheduling method based on an integrated energy system according to the present invention;
FIG. 5 is a curve of wind-solar power output and load demand at the day-ahead scheduling stage in the source-load multi-time scale optimization scheduling method based on the integrated energy system of the present invention;
FIG. 6 is a wind-solar output and load demand curve at an intra-day scheduling stage in an optimized scheduling method based on a source-load multi-time scale of an integrated energy system according to the present invention;
FIG. 7 is a diagram of the electric power optimization results of the day-ahead scheduling phase in the source-load multi-time scale optimization scheduling method based on the integrated energy system according to the present invention;
FIG. 8 is a diagram of the thermal power optimization results of the scheduling stage before the day in the source-load multi-time scale optimization scheduling method based on the integrated energy system of the present invention;
FIG. 9 is a diagram of the electric power optimization results in the scheduling phase within the day based on the source-load multi-time scale optimization scheduling method of the integrated energy system according to the present invention;
fig. 10 is a diagram of the thermal power optimization result in the scheduling stage in the source-load multi-time scale optimization scheduling method based on the integrated energy system of the present invention.
Detailed Description
The following describes in detail embodiments of the present application with reference to examples. It should be apparent that the described embodiments are only some of the embodiments of the application, and not all of the embodiments.
A source-load multi-time scale optimization scheduling method based on an integrated energy system comprises the following specific steps:
1. construction of comprehensive energy system based on multi-energy complementation
The comprehensive energy system comprises a wind turbine generator, a photovoltaic unit, an external power grid, a carbon capture power plant, a gas turbine, an electric gas conversion device, an electrolytic cell device, a hydrogen fuel cell, an electric boiler, a carbon storage and liquid storage device, a rigid load and a flexible load.
2. Analyzing the source and load sides of the comprehensive energy system based on multi-energy complementation
Comprehensive flexible operation method for source side carbon capture power plantFormula (II) is shown. The comprehensive flexible operation mode is characterized in that the carbon capture power plant can automatically and automatically use CO according to the load requirements at different time intervals by adding the combined action of the flue gas bypass system and the solution storage device compared with the independent liquid storage type and split-flow type operation modes 2 The carbon is discharged into the air, so that the scheduling flexibility is improved, and the peak clipping and valley filling can be realized, namely, the carbon capture energy consumption is transferred to the low valley period during the peak load; and the carbon capture energy consumption is increased when the load is in the valley. Therefore, the contradiction between the load demand and the carbon capture energy consumption can be solved, and the utilization of renewable energy sources is further improved; the charge side considers various demand response resources, the demand response resources comprise price type demand response resources and incentive type demand response resources, the price type demand response resources guide the user to carry out reasonable electricity utilization behaviors by changing the price of electric energy, so that the user changes the previous electricity utilization habits, and good electricity utilization guidance is generated. The price type demand response resource can change a load curve and reduce the peak-valley difference of the power load, so that the power consumption cost of a user is reduced, and the user actively participates in the peak regulation of the system. Price-type demand response resources are used herein in the day-ahead dispatch phase because of their slow rate of change with power price. Load users include rigid loads, transferable loads, interruptible loads, and alternative loads.
3. Providing a comprehensive energy system source-load multi-time scale optimization scheduling strategy based on multi-energy complementation
According to the response speed of different demand response resources to the power grid dispatching instruction, the demand response resources are divided into the following two types:
first-order demand response: the speed of the response of the demand response resources to the power grid dispatching instruction is low, the response time is usually longer than 1h, and the demand response resources need to be notified 24h in advance, so the demand response resources are used in the day-ahead dispatching stage;
secondary demand response: the speed of the response of the demand response resources to the power grid dispatching instruction is high, the response time is usually 5-15min, and the notification is required to be carried out 15-4 h in advance, so that the demand response resources are used in the day dispatching stage.
Demand response resources may vary load profilesThe output of the carbon capture power plant is optimized, and the CO of the comprehensive energy system is reduced 2 The emission is improved to capture CO in a carbon capture power plant 2 An effective means of competence. In addition, in the two stages of the day ahead and the day in, the load can be reduced to reduce the unit output and reduce CO by using different types of demand response resources at the time of load peak 2 Discharge capacity; and the load is increased in the load valley to improve the consumption of renewable energy sources and reduce the light abandoning amount of the system.
Price type demand response and first-stage demand response with low response speed to a power grid dispatching instruction are used in the day-ahead stage for a carbon capture power plant with a source side adopting a comprehensive flexible operation mode; the secondary demand response with high response speed to the power grid dispatching instruction is used in the day stage, so that the load and the rotary standby plan of the system are improved, the defect that the rotary standby capacity of the carbon capture power plant adopting a comprehensive flexible operation mode is weak is overcome, the carbon emission of the system is reduced, and the economic benefit of the system is improved.
4. Establishing comprehensive energy system source-load multi-time scale optimization scheduling model based on multi-energy complementation
And according to the proposed comprehensive energy system source-load multi-time scale optimization scheduling strategy based on the multi-energy complementation, the lowest total system cost is taken as an objective function.
1) Day ahead scheduling phase
In the current scheduling stage, the lowest total system cost is taken as a target function, the scheduling period of the proposed model is 24h, and the time scale is 1h. The objective function is:
F 1 ＝min(F H +F K +F W +F C +F S +F IDR1 ) (1)
in the formula: f H The cost of unit coal consumption; f K Punishing cost for starting and stopping the unit; f W The cost of abandoning wind and light for the system; f C A step-wise carbon trading cost; f S Cost for solution loss in the carbon capture equipment; f IDR1 To use the primary demand to respond to the resource cost.
Unit coal consumption cost F H The following formula:
in the formula: t is the total scheduling period number; u shape t The starting and stopping states of the unit at the time t are shown; a. b and c are the unit coal consumption cost coefficients respectively; p is G,t And the output power of the unit at the time t is obtained.
For a carbon capture plant, its generated power P at time t CCPP,t Firstly, the carbon capture device is supplied for use, and the electric power is called carbon capture energy consumption power P t CCT . The remaining electric power, referred to as the net output power P of the unit, is supplied to other consumers t E Their relationship is:
P CCPP,t ＝P t CCT +P t E (3)
carbon capture power consumption P t CCT Including basic power consumption
And the operation energy consumption power->
The former can be regarded as a constant, and the latter is related to the amount of carbon dioxide captured. According to the principle and schematic diagram of the comprehensive flexible operation mode of the carbon capture power plant in FIG. 2, a mathematical model of the carbon capture power plant is established as follows:
in the formula: m is a group of P,t The carbon emission is the carbon emission captured by the unit at the time t; e CG,t Carbon emissions supplied to the solution storage of the unit at time t; e G,t For the unit CO at time t 2 Total emission; p MT,t The generated power of the unit at the time t is obtained; lambda [ alpha ] CCT And
are respectively machinesThe group traps the unit CO at time t 2 Electrical energy consumed and carbon capture efficiency; delta is the flue gas split ratio of the unit; zeta is the maximum working state coefficient of compressor and regenerating tower; e.g. of the type c Is the unit carbon emission intensity of the unit.
The mathematical model of the net output power of the carbon capture power plant adopting the comprehensive flexible operation mode is as follows:
in the formula: p CCPP,t The net output power of the carbon capture power plant at the time t; p G,t The total output power of the power plant at the moment t; m is a group of cyg CO to be treated supplied to a liquid storage tank 2 The total mass.
For the carbon storage device, the mathematical model is as follows:
in the formula: m is a group of t And M t-1 The carbon storage amount of the carbon storage equipment at the time of t and t-1 is respectively; m t,out CO output by carbon storage equipment at time t 2 An amount; m min And M max The maximum carbon amount of xiao Chu of the carbon storage equipment and the maximum carbon storage amount are respectively; beta is the loss coefficient of the carbon storage equipment.
Unit start-stop punishment cost F K The following formula:
in the formula: u shape t-1 The state is the starting and stopping state of the unit at the time of t-1; c K And punishing a cost coefficient for starting and stopping the unit.
Wind and light abandoning cost F W The following formula:
in the formula: c W Abandoning cost coefficient for abandoning wind;
and &>
Respectively at time t, the wind curtailment and the light curtailment power.
Stepped carbon transaction cost F C The following formula:
the stepped carbon trading model includes a carbon emission quota model and an actual carbon emission model, and the carbon emission is divided into 5 intervals. To effectively reduce CO 2 The method establishes a stepped carbon trading mechanism, and controls the total carbon emission amount by allocating quota to the carbon emission of each unit. If the actual carbon emissions are less than the allotted carbon quota, the remaining carbon quota may be sold, otherwise, an excess portion of the carbon quota may need to be purchased.
In the formula:
a stepped carbon transaction cost for time t.
(1) Carbon emission quota model
Sources of carbon emissions in integrated energy systems include carbon capture power plants and gas turbines.
In the formula: e C 、E CCPP And E GB Respectively representing the total carbon quota of the system, the carbon quota of the carbon capture power plant unit and the carbon quota of the gas turbine; lambda [ alpha ] i And λ g Respectively the carbon quota of unit power consumption of the carbon capture power plant unit and the carbon quota of unit gas consumption of the gas unit.
(2) Actual carbon emission model
Due to the carbon capture and sequestration technology canAbsorbing a part of CO 2 The actual carbon emission model is:
in the formula: e c,a Is the total actual carbon emissions of the system; e CCPP,a And E GB,a Actual carbon emissions of the CCPP unit and the gas turbine respectively; e P2G,a CO actually absorbed by P2G plant 2 An amount; a is 1 、b 1 、c 1 And a 2 、b 2 、c 2 And respectively calculating parameters for carbon emission of the CCPP unit and the gas unit.
(3) Stepped carbon trading model
And (4) calculating the trading amount of the carbon emission right participating in the carbon trading market according to the carbon emission quota and the actual carbon emission.
E C,t ＝E C,a -E C (12)
In the formula: e C,t Trading the total carbon emission rights of the system.
The stepwise carbon trading model established herein divides carbon emissions into a plurality of intervals, and as the purchased carbon quota is greater, the purchase price of the corresponding interval is also higher. The stepwise carbon trading model is:
in the formula:
a step-wise carbon trading cost; theta is the basic price of carbon trading; l is the length of the carbon emission interval; beta is the rate of price increase.
Cost of solution loss F S The following formula:
in the formula: c S Is the cost factor of the solvent; transporting psi as solventThe line loss factor.
First order demand response resource cost F IDR1 The following formula:
in the formula: c IDR1 Adopting a first-level demand response resource cost coefficient; p is IDR1,t The primary demand response resource total is called at time t.
The system balance constraints are:
power balance constraint
In the formula: p PBDR,t The load power is the load power after PBDR resource calling at the time t; p is WT,t And P PV,t Respectively wind power output and photovoltaic output at the time t; n is the type of different units in IES; p i,t The net output power of the unit i at the time t.
Wind and light output constraint
In the formula:
and &>
And respectively predicting the output for wind and light at the time t.
Carbon capture power plant operation constraints
(1) Unit output constraint
In the formula: p CCPP,min And P CCPP,max Respectively the minimum output and the maximum output of the unit.
(2) Unit climbing restraint
R down ≤P CCPP,t -P CCPP,t-1 ≤R up (19)
In the formula: r up And R down The up and down climbing speeds of the unit are respectively.
(3) Unit start-stop restraint
In the formula:
and &>
Respectively the continuous startup time and shutdown time of the unit at the time of t-1; />
And
the minimum startup time and the minimum shutdown time of the unit are respectively.
(4) Solution reservoir related constraints
In the formula: v F,t And V F,t-1 The solution volumes of the rich solution storage at the time t and the time t-1 of the unit are respectively; v P,t And V P,t-1 The solution volumes of the barren solution storage at the time t and the time t-1 of the unit are respectively; v C,t The volume of solution required for the solution reservoir to discharge carbon dioxide at time t; v RY The volume of the solution storage; v F,0 And V F,24 Respectively the initial solution volume of the rich solution storage and the solution volume after the dispatching cycle is finished; v P,0 And V P,24 Respectively the initial solution volume of the barren liquor storage and the solution after the end of the dispatching cycleVolume.
In the formula: m MEA And M C The molar mass of the ethanolamine solution and the molar mass of the carbon dioxide are respectively; phi is the analysis amount of the regeneration tower; k R Is the concentration coefficient of the ethanolamine solution; rho R Is the density of the ethanolamine solution.
Gas turbine constraints
In the formula: q g-h,t The heating power of the gas turbine device at time t;
the upper limit of the heating power of the gas turbine plant at time t.
Hydrogen fuel cell constraints
In the formula: p e-c,t The electric power consumed by the electrolytic cell device at the moment t;
the upper limit of the output force of the electrolytic cell device at the moment t; p HFC,t And Q HFC,t The electric output power and the heat output power of the hydrogen fuel cell at the time t are respectively; />
And &>
The upper limit of the electrical power and the upper limit of the thermal power of the hydrogen fuel cell at the time t are respectively.
Electric to gas equipment restraint
In the formula:
and &>
Respectively carrying out electricity-to-gas hydrogen consumption power and electricity-to-gas natural gas generation power on the electricity-to-gas equipment at the time t; />
The upper limit of the hydrogen consumption power of the electric gas conversion equipment is set; />
And the upper limit of the output of the electric gas conversion equipment.
Restraint of stored energy
Mathematical models of different energy storage devices in the integrated energy system are similar, so that the five types of energy storage devices in the text are uniformly modeled.
In the formula: e es,i,t And P es,i,t The energy storage capacity and the energy storage power of the ith energy storage device at the moment t are respectively; tau is the energy loss rate of different energy storage devices; p ch,i And P dis,i Respectively charging and discharging the energy storage power of the ith energy storage device; eta ch,i And η dis,i Respectively charging and discharging the energy storage efficiency of the ith energy storage device; v es,i The total capacity of the ith energy storage device; lambda [ alpha ] min,i And λ max,i Respectively the energy storage minimum and maximum charge states of the ith energy storage device; delta. For the preparation of a coating ch,i And delta dis,i The maximum charging and discharging rates of the ith energy storage device are respectively set;
and &>
Respectively charging and discharging the ith energy storage deviceA lower and upper power limit; mu.s ch,t And mu dis,t Respectively an energy storage state variable and an energy discharge state variable; />
And &>
The lower limit and the upper limit of the capacity of the ith energy storage device are respectively.
Demand response resource constraints
The PBDR resource herein uses peak-to-valley time-of-use electricity prices to build a mathematical model with the constraints:
in the formula: λ q is a load change rate matrix; e is an elasticity requirement matrix; dp is the electricity price rate of change matrix.
The usage of the demand response resource is related to the response speed and the response capacity, and the constraint conditions are as follows:
in the formula:
maximum response for the first level demand response load; v IDR1 The response rate for a first order demand response load.
Rotational back-up restraint
In the formula:
and &>
Respectively the upper limit and the lower limit of net output of the unit; />
And &>
Respectively the upper rotation spare quantity and the lower rotation spare quantity needed by the system at the time t.
Junctor interaction power constraints
In the formula: p is ex,t The power of the tie line interaction at the moment t;
and &>
Respectively the minimum power and the maximum power of the crosstie interaction.
2) Scheduling phase within day
In the scheduling stage in this day, the lowest total system cost is taken as a target function, the scheduling period of the proposed model is 24h, and the time scale is 15min. Compared with the scheduling stage in the day ahead, the scheduling stage needs to consider the calling cost and the load loss cost of the secondary demand response resources, and does not need to consider the calling cost and the unit start-stop cost of the primary demand response resources. The objective function is:
F 2 ＝min(F H +F W +F C +F S +F q +F IDR2 ) (31)
in the formula: f q For lost load costs; f IDR2 To use secondary demand to respond to resource costs.
Because the model of the unit coal consumption cost, the system wind and light abandoning cost, the stepped carbon transaction cost and the solution loss cost in the carbon capture equipment in the section is similar to the model in the day-ahead scheduling stage, the description is not repeated in the section.
Cost of lost load
In the formula: c q Is a unit load loss cost coefficient; p q,t The unit load loss power at the time of t time period.
Secondary demand response resource cost
In the formula: c IDR2 Adopting a second-level demand response resource cost coefficient; p IDR2,t The total amount of secondary demand response resources is called at time t.
The system balance constraints are:
the wind-light output constraints, the carbon capture plant operation constraints, the gas turbine constraints, the hydrogen fuel cell constraints, the electric-to-gas equipment constraints, the energy storage constraints, the demand response resource constraints, and the tie line interactive power constraints in this section are similar to the constraints in the day-ahead scheduling phase, and therefore, the description thereof will not be repeated.
Power balance constraint
In the formula: p is RN,t Predicting power of the load at the moment t in the day period; delta P PBDR,t The price type demand response resource response quantity determined at the time t in the day-ahead stage.
Rotational back-up restraint
In addition, the application provides an example analysis to verify the effectiveness of the comprehensive energy system source-load multi-time scale optimization scheduling method based on the multi-energy complementation, the example provides four schemes for analysis and verification, and the specific scheme is as follows:
scheme 1: various devices in the comprehensive energy system operate independently, multi-time scale optimization scheduling of a system of the shunting type carbon capture power plant is considered, and demand response resources are not considered;
scheme 2: various devices in the comprehensive energy system adopt a combined operation mode, the electricity-to-gas two-stage operation process is not refined, the multi-time scale optimization scheduling of the system of the shunting type carbon capture power plant is considered, and demand response resources are considered;
scheme 3: various devices in the comprehensive energy system adopt a combined operation mode, the two-stage operation process of electricity-gas conversion is not detailed, the multi-time scale optimization scheduling of the system of the carbon capture power plant in a comprehensive flexible operation mode is considered, and demand response resources are considered;
scheme 4: various devices in the comprehensive energy system adopt a combined operation mode, the electricity-to-gas two-stage operation process is refined, the multi-time scale optimization scheduling of the system of the carbon capture power plant in a comprehensive flexible operation mode is considered, and demand response resources are considered.
Fig. 7 shows the optimal operation result of the electric power of the comprehensive energy system in the scheduling stage at the present day, and it can be seen that the electric power system can meet the demand of the electric load, and the energy consumption distribution of the user is more reasonable due to the addition of the demand response resource in the system. At 0-6 moment, hydrogen fuel cells, wind power, a carbon capture power plant unit and a gas turbine are used for supplying power at night, the electric load requirement is met, and redundant electric power is used for hydrogen production storage of electrolyzed water; at the moment of 7-15, the electric load demand is increased in the daytime, the power generation proportion of renewable energy sources is increased, the photovoltaic power supply is started, and the output of the carbon capture power plant unit is reduced; at 16-18 moments, the photovoltaic output is gradually reduced, and the wind power, the hydrogen fuel cell and the carbon capture power plant unit supply power to meet the electric power balance; and at the time 19-24, the wind power output is gradually increased, and the residual electric power is used for hydrogen production and storage by water electrolysis. In the dispatching cycle, the power supply of various energy sources finally meets the requirement of the electric load.
Fig. 8 shows the optimal operation result of the thermal power of the comprehensive energy system in the scheduling stage at the present day, and it can be seen that, because demand response resources are added in the system, the energy consumption distribution of users is more reasonable, and the thermodynamic system can meet the demand of the thermal load. At 0-6 moment, the heat load is large at night, and the hydrogen fuel cell, the gas turbine and the electric boiler output the heat load with the maximum heat output; at the time 7-18, along with the increase of the illumination intensity in the daytime, the electric load is increased, the heat load is reduced, and the heat output of the gas turbine and the electric boiler is reduced; at the time 19-24, the illumination intensity is gradually reduced, and the heat supply output of the hydrogen fuel cell, the gas turbine and the electric boiler is gradually increased, so that the heat power balance is realized. In the scheduling period, the hydrogen fuel cell, the gas turbine and the electric boiler are matched with each other, and finally the requirement of the heat load is met.
Fig. 9 and fig. 10 show the optimal operation results of the electric power and the thermal power of the comprehensive energy system in the scheduling stage in the next day in the scheme, and it can be seen that the electric power system and the thermal power system can meet the requirements of the electric load and the thermal load, and the energy utilization distribution of users is more reasonable due to the addition of the demand response resource in the system. The deviation of the scheduling plan and prediction of the day-ahead scheduling stage can be corrected in the day-ahead scheduling stage, and compared with the day-ahead scheduling stage, the prediction of the unit output and the load demand is more accurate, so that the total cost of the system is reduced, the total carbon emission of the system is reduced, and the consumption of renewable energy is improved.
TABLE 1 run results of different scenarios in the scheduling phase before the day
Data analysis is performed below by comparing data between the schemes of table 1.
In the day-ahead scheduling stage, compared with the scheme 1 in the scheme 2, various devices in the comprehensive energy system adopt a combined operation mode, demand response resources are considered, the coal consumption cost of a unit and the first-level demand response resource cost are increased, but the CO of the system is reduced by the combined operation mode 2 The emission, the wind and light abandoning cost of the system and the start-stop cost of the unit are increased, and the carbon trading gain of the system is increased, so that the total cost of the system is reduced. Compared with the scheme 2, the scheme 3 considers the comprehensive flexible operation mode of the carbon capture power plant, although the coal consumption cost of the unit is increased, the CO of the system is further reduced 2 The emission and the start-stop cost of the unit are increased, the carbon trading gain of the system is increased, and the carbon trading gain is further reducedThe overall cost of the system, thus demonstrating the superiority of the comprehensive flexible operating mode of the carbon capture plant. Scheme 4 considers the two-stage operation process of refining electricity-to-gas based on scheme 3, and further improves the capture amount and CO consumption of the carbon capture equipment 2 The hydrogen energy advantage is better played, the carbon emission of the system is the lowest under the scheme 4, the total cost is optimal, the low carbon performance and the economic performance of the system are improved, the combined operation mode of various devices in the comprehensive energy system is verified, the electricity-to-gas two-stage operation process is refined, and the comprehensive flexible operation mode of the carbon capture power plant and the effectiveness of demand response resources are considered.
Table 2 operation results of different schemes in scheduling stage within day
Data analysis is performed below by comparing data between the schemes of table 2.
In the scheduling stage in the day, compared with the scheme 1 in the scheme 2, various devices in the comprehensive energy system adopt a combined operation mode, demand response resources are considered, the carbon emission, the unit coal consumption cost, the wind and light abandoning cost and the total cost are all lower than those of the scheme 1, and the effectiveness of adopting the combined operation mode and considering the demand response resources is proved. Scheme 3 considers the integrated flexible operation of the carbon capture plant compared to scheme 2, which is lower than scheme 2 in both carbon emissions and overall cost, thus demonstrating the effectiveness of the integrated flexible operation of the carbon capture plant. Compared with the scheme 3, the scheme 4 refines the electricity-to-gas operation process into the electricity-to-gas two-stage operation process, and further improves the capture amount and CO consumption of the carbon capture equipment 2 The amount, and better the exploitation of the hydrogen energy advantage, is lower than scheme 3 in terms of carbon emissions, unit coal consumption cost, and overall cost, thus demonstrating the superiority of the scheduling scheme herein. Under the scheduling stage, no load loss situation occurs in any of the four schemes.
Although the embodiments of the present invention have been described above, the present invention is not limited to the above embodiments, and any modifications within the scope of the present invention can be made by those skilled in the art.
Claims (8)
1. A source-load multi-time scale optimization scheduling method based on an integrated energy system is characterized by comprising the following steps:
step A: firstly, source and load sides are coordinated and matched, and demand response resources are divided into a first-level demand response resource and a second-level demand response resource according to the response speed of different demand response resources to a power grid dispatching instruction. The first-level demand response resources are used in the day-ahead scheduling stage, and the second-level demand response resources are used in the day-inside scheduling stage;
and B: a carbon capture power plant is formed by modifying a traditional thermal power generating unit with an additional carbon capture device on the source side, and the carbon capture power plant adopts a comprehensive flexible operation mode. The comprehensive flexible operation mode is that the carbon capture power plant is added with a flue gas bypass system and a solution storage to act together;
and C: the load side considers various demand response resources, the demand response resources comprise price type demand response resources and incentive type demand response resources, and the load users comprise rigid loads, transferable loads, interruptible loads and replaceable loads;
step D: acquiring initial carbon emission quota and actual carbon emission quota of the comprehensive energy system, and constructing a stepped carbon trading model of the comprehensive energy system according to the initial carbon emission quota and the actual carbon emission quota;
step E: on the basis of the step A, calling the resources on the two sides of the source side in the step B and the step C, considering the step-type carbon trading model in the step D, and establishing a source load coordination day-ahead-day internal two-stage low-carbon economic optimization scheduling model;
step F: and E, based on the source-load coordination day-ahead-day-intra-two-stage low-carbon economic optimization scheduling model in the step E, improving an IEEE-39 node as an example simulation, and solving the model by utilizing solver CPLEX software in MATLAB software.
2. The source-load multi-time scale optimization scheduling method based on the integrated energy system according to claim 1, characterized in that: the comprehensive energy system in the step A comprises a wind turbine generator, a photovoltaic generator, an external power grid, a carbon capture power plant, a gas turbine, an electric-to-gas device, an electrolytic cell device, a hydrogen fuel cell, an electric boiler, a carbon storage and liquid storage device, a rigid load and a flexible load.
3. The source-load multi-time scale optimization scheduling method based on the integrated energy system according to claim 1, characterized in that: the demand response resource types in the step A are divided according to the response speed of different demand response resources to the power grid dispatching instruction.
4. The source-load multi-time scale optimization scheduling method based on the integrated energy system according to claim 1, characterized in that: the first-level comprehensive demand response in the step A is as follows: the speed of the comprehensive demand response resources for responding to the power grid dispatching instruction is low, the response time is usually longer than 1h, and the comprehensive demand response resources need to be notified 24h in advance, so the comprehensive demand response resources are used in the day-ahead dispatching stage; secondary comprehensive demand response: the comprehensive demand response resources have high response speed to the power grid dispatching instruction, the response time is usually 5-15min, and the notification needs to be carried out 15-4 h in advance, so the comprehensive demand response resources are used in the day dispatching stage.
5. The source-load multi-time scale optimization scheduling method based on the integrated energy system according to claim 1, characterized in that: the source side carbon capture power plant in the step B adopts a comprehensive flexible operation mode, the comprehensive flexible operation mode is that the carbon capture power plant is added with a flue gas bypass system and a solution storage to act together, and compared with an independent liquid storage mode and a split-flow operation mode, the comprehensive flexible operation mode can automatically discharge CO2 into the air according to the load requirements at different time intervals, improves the scheduling flexibility, and can realize 'peak clipping and valley filling', namely, the carbon capture energy consumption is transferred to a low valley period when the load is in a peak; and the carbon capture energy consumption is increased in the load valley, so that the contradiction between the load demand and the carbon capture energy consumption can be solved, and the utilization of renewable energy sources is further improved.
6. The source-load multi-time scale optimization scheduling method based on the integrated energy system according to claim 1, characterized in that: the load side in the step C considers various demand response resources, the demand response resources comprise price type demand response resources and incentive type demand response resources, the price type demand response resources guide the user to carry out reasonable electricity utilization behaviors by changing the price of electric energy, so that the user changes the electricity utilization habits before and generates good electricity utilization guidance, the price type demand response resources can change a load curve and reduce the peak-valley difference of the electric load, the electricity utilization cost of the user is reduced, the user actively participates in the peak shaving of the system, and the price type demand response resources are used in the day-ahead scheduling stage because the speed of the price type demand response resources changing along with the price of the electric energy is slow, and the load user comprises a rigid load, a transferable load, an interruptible load and an alternative load.
7. The source-load multi-time scale optimization scheduling method based on the integrated energy system according to claim 1, characterized in that: and D, the step-type carbon trading model in the step D comprises an initial carbon emission quota, an actual carbon emission quota and a carbon emission price, and the carbon emission right trading volume participating in the carbon trading market can be obtained according to the carbon emission quota and the actual carbon emission.
8. The source-load multi-time scale optimization scheduling method based on the integrated energy system according to claim 1, characterized in that: the comprehensive energy system source-load multi-time scale optimization scheduling method based on multi-energy complementation further comprises constraints on all links, including power balance constraint, unit operation constraint, constraint of all devices in the comprehensive energy system and tie line interaction power constraint.
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